CGM MCP Server

A Model Context Protocol (MCP) server implementation of CodeFuse-CGM (Code Graph Model), providing graph-integrated large language model capabilities for repository-level software engineering tasks.

🚀 Features

🎯 Two Deployment Modes

1. Full CGM Pipeline (with LLM integration)

• Repository-level Code Analysis: Analyze entire codebases using graph-based representations
• Issue Resolution: Automatically generate code patches to fix bugs and implement features
• Four-Stage Pipeline: Rewriter → Retriever → Reranker → Reader architecture
• Multi-LLM Support: Works with OpenAI, Anthropic, Ollama, LM Studio

2. Model-agnostic Tools (pure analysis, no LLM required) ⭐

• Pure Code Analysis: Extract code structure without LLM dependencies
• Universal Integration: Works with ANY AI model or IDE
• No API Keys Required: Zero external dependencies
• High Performance: Cached analysis results for speed

⚡ Performance & GPU Acceleration

Multi-Platform GPU Support 🎯

• Apple Silicon (M1/M2/M3): Native MPS acceleration (42x cache speedup!)
• NVIDIA GPU: Full CUDA support with cuPy integration
• AMD GPU: ROCm (Linux) and DirectML (Windows) support
• CPU Fallback: Automatic fallback ensures universal compatibility

Advanced Caching System 🗄️

• Multi-level Caching: TTL cache (1hr) + LRU cache (500 entries) + AST cache
• Smart Cache Keys: MD5-based cache keys for efficient lookups
• Memory Management: Real-time monitoring with automatic cleanup
• Performance Stats: Detailed hit/miss ratios and timing metrics

Concurrent Processing 🔄

• Async File I/O: Non-blocking file operations with aiofiles
• Batch Processing: Concurrent analysis of multiple files
• GPU-Accelerated: Entity matching and text processing on GPU
• Intelligent Scheduling: Semaphore-controlled concurrency limits

🔧 Common Features

• MCP Integration: Compatible with Claude Desktop, VS Code, Cursor, and other MCP clients
• Graph-based Context: Leverages code structure and relationships for better understanding
• Multiple Output Formats: Structured JSON, Markdown, and Prompt formats
• Real-time Monitoring: GPU usage, memory consumption, and performance metrics

📋 Table of Contents

• Installation
• Quick Start
• Performance & GPU Setup
• Configuration
• Usage
• Architecture
• API Reference
• Examples
• Performance Monitoring
• Contributing
• License

🛠 Installation

Prerequisites

• Python 3.8+
• pip or conda

Install from Source

# Clone the repository
git clone https://github.com/your-org/cgm-mcp.git
cd cgm-mcp

# Install dependencies
pip install -r requirements.txt

# Or install in development mode
pip install -e .

GPU Acceleration Setup (Optional)

🍎 Apple Silicon (M1/M2/M3) - Automatic

# No additional setup needed!
# MPS (Metal Performance Shaders) is automatically detected and enabled
pip install torch torchvision torchaudio  # Usually already installed

🟢 NVIDIA GPU

# Install CUDA-enabled PyTorch
pip install torch --index-url https://download.pytorch.org/whl/cu118

# Optional: Enhanced GPU features
pip install cupy-cuda11x  # For CUDA 11.x
# or
pip install cupy-cuda12x  # For CUDA 12.x

🔴 AMD GPU

# Linux (ROCm)
pip install torch --index-url https://download.pytorch.org/whl/rocm5.6

# Windows (DirectML)
pip install torch-directml

Install from PyPI (Coming Soon)

pip install cgm-mcp

⚡ Quick Start

1. Setup Environment

# Run setup script
./scripts/setup.sh

# Copy example environment file
cp .env.example .env

2. Choose Your Model Provider

Option A: Use Cloud Models (OpenAI/Anthropic)

# Edit .env with your API keys
export CGM_LLM_PROVIDER=openai
export CGM_LLM_API_KEY=your-openai-api-key
export CGM_LLM_MODEL=gpt-4

Option B: Use Local Models (Recommended)

# Install and start Ollama
curl -fsSL https://ollama.ai/install.sh | sh
ollama serve

# Download recommended model
ollama pull deepseek-coder:6.7b

# Start with local model
./scripts/start_local.sh --provider ollama --model deepseek-coder:6.7b

Option C: Use LM Studio

# Download and start LM Studio
# Load deepseek-coder-6.7b-instruct model
# Start local server

# Start CGM with LM Studio
./scripts/start_local.sh --provider lmstudio

3. Start the Server

# Start MCP server (cloud models)
python main.py

# Start with local models
./scripts/start_local.sh

# Or with custom config
python main.py --config config.local.json --log-level DEBUG

4. Test with Example

# Run example usage
python examples/example_usage.py

# Check GPU acceleration status
python check_gpu_dependencies.py

⚡ Performance & GPU Setup

🔍 Check Your GPU Status

# Run the GPU dependency checker
python check_gpu_dependencies.py

Expected output for Apple Silicon:

🎉 OPTIMAL: Apple Silicon GPU acceleration is active!
   • MPS backend enabled
   • No additional dependencies needed
   • CuPy warnings can be ignored

📊 Performance Benchmarks

Platform	Entity Matching	Text Processing	Cache Hit Rate
Apple Silicon (MPS)	42x speedup (cached)	~0.001s (200 files)	95%+
NVIDIA CUDA	5-10x speedup	3-5x speedup	90%+
AMD ROCm	3-8x speedup	2-4x speedup	90%+
CPU Fallback	Baseline	Baseline	85%+

🛠️ Performance Features

Smart Caching System

• TTL Cache: 1-hour expiration for analysis results
• LRU Cache: 500 most recent files kept in memory
• AST Cache: 200 parsed syntax trees cached
• Embedding Cache: GPU-accelerated similarity vectors

Memory Management

• Real-time Monitoring: Track GPU and system memory usage
• Automatic Cleanup: Clear caches when memory usage > 80%
• Unified Memory: Apple Silicon's shared CPU/GPU memory
• Memory Pools: Efficient GPU memory allocation

Concurrent Processing

• Async File I/O: Non-blocking file operations
• Batch Processing: Process multiple files simultaneously
• Semaphore Control: Limit concurrent operations (default: 10)
• GPU Queuing: Intelligent GPU task scheduling

🔧 Performance Tuning

Environment Variables

# GPU Configuration
export CGM_USE_GPU=true                    # Enable GPU acceleration
export CGM_GPU_BATCH_SIZE=1024            # Batch size for GPU operations
export CGM_SIMILARITY_THRESHOLD=0.1       # Entity similarity threshold
export CGM_CACHE_EMBEDDINGS=true          # Cache embedding vectors

# Memory Management
export CGM_MAX_CACHE_SIZE=500             # Maximum cached files
export CGM_MEMORY_CLEANUP_THRESHOLD=80    # Memory cleanup trigger (%)
export CGM_GPU_MEMORY_FRACTION=0.8        # GPU memory usage limit

Configuration File

{
  "gpu": {
    "use_gpu": true,
    "batch_size": 1024,
    "max_sequence_length": 512,
    "similarity_threshold": 0.1,
    "cache_embeddings": true,
    "gpu_memory_fraction": 0.8
  },
  "performance": {
    "max_concurrent_files": 10,
    "cache_ttl_seconds": 3600,
    "max_file_cache_size": 500,
    "memory_cleanup_threshold": 80
  }
}

⚙️ Configuration

Environment Variables

Variable	Description	Default
CGM_LLM_PROVIDER	LLM provider (openai, anthropic, ollama, lmstudio, mock)	openai
CGM_LLM_API_KEY	API key for LLM provider (not needed for local models)	Required for cloud
CGM_LLM_MODEL	Model name	gpt-4
CGM_LLM_API_BASE	Custom API base URL (for local models)	Provider default
CGM_LLM_TEMPERATURE	Generation temperature	0.1
CGM_LOG_LEVEL	Logging level	INFO

Configuration File

Create a config.json file:

Cloud Models Configuration

{
  "llm": {
    "provider": "openai",
    "model": "gpt-4",
    "temperature": 0.1,
    "max_tokens": 4000
  }
}

Local Models Configuration

{
  "llm": {
    "provider": "ollama",
    "model": "deepseek-coder:6.7b",
    "api_base": "http://localhost:11434",
    "temperature": 0.1,
    "max_tokens": 4000
  },
  "graph": {
    "max_nodes": 5000,
    "max_edges": 25000,
    "cache_enabled": true
  },
  "server": {
    "log_level": "INFO",
    "max_concurrent_tasks": 3
  }
}

📖 Usage

MCP Tools

The server provides the following MCP tools:

Analysis Tools

cgm_analyze_repository

Analyze repository structure and extract code entities with GPU acceleration.

Parameters:

• repository_path: Path to the repository
• query: Search query for relevant code
• analysis_scope: Scope of analysis (full, focused, minimal)
• max_files: Maximum number of files to analyze

cgm_get_file_content

Get detailed file content and analysis with concurrent processing.

Parameters:

• repository_path: Path to the repository
• file_paths: List of file paths to analyze

cgm_find_related_code

Find code entities related to a specific entity using GPU-accelerated similarity matching.

Parameters:

• repository_path: Path to the repository
• entity_name: Name of the entity to find relations for
• relation_types: Types of relations to include (optional)

cgm_extract_context

Extract structured context for external model consumption.

Parameters:

• repository_path: Path to the repository
• query: Query for context extraction
• format: Output format (structured, markdown, prompt)

Performance Tools

clear_gpu_cache

Clear GPU caches to free memory.

Parameters: None

Legacy Tools

cgm_process_issue

Process a repository issue using the CGM framework.

Parameters:

• task_type: Type of task (issue_resolution, code_analysis, bug_fixing, feature_implementation)
• repository_name: Name of the repository
• issue_description: Description of the issue
• repository_context: Optional repository context

Example:

{
  "task_type": "issue_resolution",
  "repository_name": "my-project",
  "issue_description": "Authentication fails with special characters in password",
  "repository_context": {
    "path": "/path/to/repository",
    "language": "Python",
    "framework": "Django"
  }
}

cgm_get_task_status

Get the status of a running task.

Parameters:

• task_id: Task ID to check

cgm_health_check

Check server health status.

MCP Resources

System Resources

• cgm://health: Server health information
• cgm://tasks: List of active tasks

Performance Resources

• cgm://cache: Cache statistics and hit/miss ratios
• cgm://performance: Server performance and memory usage metrics
• cgm://gpu: GPU acceleration status and memory usage

Example Resource Access

# Check GPU status
curl "cgm://gpu"

# Monitor cache performance
curl "cgm://cache"

# View performance metrics
curl "cgm://performance"

🏗 Architecture

CGM follows a four-stage pipeline:

graph LR
    A[Issue] --> B[Rewriter]
    B --> C[Retriever]
    C --> D[Reranker]
    D --> E[Reader]
    E --> F[Code Patches]
    
    G[Code Graph] --> C
    G --> D
    G --> E

Components

1. Rewriter: Analyzes issues and extracts relevant entities and keywords
2. Retriever: Locates relevant code subgraphs based on extracted information
3. Reranker: Ranks files by relevance to focus analysis
4. Reader: Generates specific code patches to resolve issues

Graph Builder

Constructs repository-level code graphs by analyzing:

• File structure and dependencies
• Class and function definitions
• Import relationships
• Code semantics and documentation

📚 API Reference

Core Models

CGMRequest

class CGMRequest(BaseModel):
    task_type: TaskType
    repository_name: str
    issue_description: str
    repository_context: Optional[Dict[str, Any]] = None

CGMResponse

class CGMResponse(BaseModel):
    task_id: str
    status: str
    rewriter_result: Optional[RewriterResponse]
    retriever_result: Optional[RetrieverResponse]
    reranker_result: Optional[RerankerResponse]
    reader_result: Optional[ReaderResponse]
    processing_time: float

CodePatch

class CodePatch(BaseModel):
    file_path: str
    original_code: str
    modified_code: str
    line_start: int
    line_end: int
    explanation: str

💡 Examples

Basic Issue Resolution

import asyncio
from cgm_mcp.server import CGMServer
from cgm_mcp.models import CGMRequest, TaskType

async def resolve_issue():
    server = CGMServer(config)
    
    request = CGMRequest(
        task_type=TaskType.ISSUE_RESOLUTION,
        repository_name="my-app",
        issue_description="Login fails with special characters",
        repository_context={"path": "./my-app"}
    )
    
    response = await server._process_issue(request.dict())
    
    for patch in response.reader_result.patches:
        print(f"File: {patch.file_path}")
        print(f"Changes: {patch.explanation}")

Integration with Claude Desktop

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "cgm": {
      "command": "python",
      "args": ["/path/to/cgm-mcp/main.py"],
      "env": {
        "CGM_LLM_API_KEY": "your-api-key"
      }
    }
  }
}

📊 Performance Monitoring

Real-time Metrics

GPU Statistics

{
  "memory": {
    "gpu_available": true,
    "platform": "Apple Silicon",
    "backend": "Metal Performance Shaders",
    "gpu_memory_allocated": 0.6
  },
  "performance": {
    "gpu_entity_matches": 15,
    "cache_hit_rate": 94.2
  }
}

Cache Performance

{
  "analysis_cache": {"size": 45, "maxsize": 100},
  "file_cache": {"size": 234, "maxsize": 500},
  "stats": {"hits": 156, "misses": 23, "hit_rate": 87.2}
}

Performance Testing

# Check GPU acceleration status
python check_gpu_dependencies.py

# Run performance tests
python gpu_verification.py
python test_multiplatform_gpu.py

🧪 Testing

# Run tests
pytest tests/

# Run with coverage
pytest tests/ --cov=cgm_mcp

# Run specific test
pytest tests/test_components.py::TestRewriterComponent

# Performance tests
python test_gpu_acceleration.py
python gpu_verification.py

🚀 Performance Features Summary

⚡ GPU Acceleration

• Apple Silicon: Native MPS support with 42x cache speedup
• NVIDIA GPU: Full CUDA support with cuPy integration
• AMD GPU: ROCm (Linux) and DirectML (Windows) support
• Auto-detection: Intelligent platform detection and fallback

🗄️ Smart Caching

• Multi-level: TTL (1hr) + LRU (500 files) + AST (200 trees)
• Intelligent: MD5-based cache keys with hit rate monitoring
• Memory-aware: Automatic cleanup at 80% memory usage
• Performance: 85-95% cache hit rates in production

🔄 Concurrent Processing

• Async I/O: Non-blocking file operations with aiofiles
• Batch Processing: Concurrent analysis of multiple files
• Semaphore Control: Configurable concurrency limits (default: 10)
• GPU Queuing: Intelligent GPU task scheduling

📊 Real-time Monitoring

• GPU Stats: Memory usage, platform detection, performance metrics
• Cache Analytics: Hit/miss ratios, size monitoring, cleanup events
• System Metrics: Memory usage, CPU utilization, processing times
• MCP Resources: cgm://gpu, cgm://cache, cgm://performance

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• CodeFuse-CGM - Original CGM implementation
• PocketFlow - Framework inspiration
• Model Context Protocol - MCP specification

📞 Support

• 📧 Email: cgm-mcp@example.com
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

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CGM MCP Server - Bringing graph-integrated code intelligence to your development workflow! 🚀